The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Lithium-ion batteries are employed to provide high power and high energy densities in portable applications including, e.g., mobile devices, computing devices, and propulsion systems for vehicles. Power and energy management of lithium-ion batteries relies upon accurate determination of battery parameters including state of charge (SOC) and state of health (SOH) in real-time. Known systems for determining SOC and SOH may include an adaptive algorithm to provide real time prediction of SOC and SOH with associated errors due to inaccuracies in estimating the SOC.
SOC refers to stored electrical charge of a battery system, indicating available electric power for work relative to that which is available when the battery is fully charged. SOC may be viewed as a thermodynamic quantity, enabling one to assess the potential energy of the system. SOC may be used for purposes of regulating power flow from the battery pack to generate mechanical work, balanced with mechanical power originating from an internal combustion engine.
To better control the propulsion battery systems in vehicles for long battery life and good fuel economy, onboard systems determine and process battery parameters such as the open-circuit voltage (OCV), battery ohmic resistance, battery capacitance, and other parameters to determine SOC. However, OCV and other battery internal parameters are not directly measurable during vehicle operation.
It is known in the art to use a predetermined calibration table to regulate a battery pack, which has pre-determined parameters that are based on a standard vehicle or an experimental vehicle. It is known to use Coulomb counting to determine an SOC value of a battery system. Coulomb counting may be implemented when an initial SOC and a current efficiency is known, which may have inaccuracies. It is known to use differential voltage analysis, i.e., dV/dQ vs. V, to determine the source of capacity fade for lithium-ion batteries. It is known to use differential charge analysis, i.e., dQ/dV vs. Q, to determine the capacity fade for lithium-ion batteries and to quantify the composition change in materials.